Analytics Model

Code
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import adjusted_rand_score
eda = pd.read_parquet("data/eda.parquet")
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[1], line 8
      6 from sklearn.preprocessing import LabelEncoder
      7 from sklearn.metrics import adjusted_rand_score
----> 8 eda = pd.read_parquet("data/eda.parquet")

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:667, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)
    664     use_nullable_dtypes = False
    665 check_dtype_backend(dtype_backend)
--> 667 return impl.read(
    668     path,
    669     columns=columns,
    670     filters=filters,
    671     storage_options=storage_options,
    672     use_nullable_dtypes=use_nullable_dtypes,
    673     dtype_backend=dtype_backend,
    674     filesystem=filesystem,
    675     **kwargs,
    676 )

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:267, in PyArrowImpl.read(self, path, columns, filters, use_nullable_dtypes, dtype_backend, storage_options, filesystem, **kwargs)
    264 if manager == "array":
    265     to_pandas_kwargs["split_blocks"] = True  # type: ignore[assignment]
--> 267 path_or_handle, handles, filesystem = _get_path_or_handle(
    268     path,
    269     filesystem,
    270     storage_options=storage_options,
    271     mode="rb",
    272 )
    273 try:
    274     pa_table = self.api.parquet.read_table(
    275         path_or_handle,
    276         columns=columns,
   (...)
    279         **kwargs,
    280     )

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/parquet.py:140, in _get_path_or_handle(path, fs, storage_options, mode, is_dir)
    130 handles = None
    131 if (
    132     not fs
    133     and not is_dir
   (...)
    138     # fsspec resources can also point to directories
    139     # this branch is used for example when reading from non-fsspec URLs
--> 140     handles = get_handle(
    141         path_or_handle, mode, is_text=False, storage_options=storage_options
    142     )
    143     fs = None
    144     path_or_handle = handles.handle

File /opt/hostedtoolcache/Python/3.11.12/x64/lib/python3.11/site-packages/pandas/io/common.py:882, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    873         handle = open(
    874             handle,
    875             ioargs.mode,
   (...)
    878             newline="",
    879         )
    880     else:
    881         # Binary mode
--> 882         handle = open(handle, ioargs.mode)
    883     handles.append(handle)
    885 # Convert BytesIO or file objects passed with an encoding

FileNotFoundError: [Errno 2] No such file or directory: 'data/eda.parquet'
Code
features = eda[['SALARY', 'MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE']].copy()

for col in ['MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE', 'SALARY']:
    features[col] = pd.to_numeric(features[col], errors='coerce')

features = features.dropna()

scaler = StandardScaler()
X = scaler.fit_transform(features)

kmeans = KMeans(n_clusters=4, random_state=688)
eda.loc[features.index, 'Cluster'] = kmeans.fit_predict(X)

true_labels = eda.loc[features.index, 'SOC_2021_4_NAME']
true_labels_encoded = LabelEncoder().fit_transform(true_labels)

ari = adjusted_rand_score(true_labels_encoded, eda.loc[features.index, 'Cluster'])
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[2], line 1
----> 1 features = eda[['SALARY', 'MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE']].copy()
      3 for col in ['MAX_YEARS_EXPERIENCE', 'MIN_YEARS_EXPERIENCE', 'SALARY']:
      4     features[col] = pd.to_numeric(features[col], errors='coerce')

NameError: name 'eda' is not defined
Code
import plotly.express as px
import plotly.graph_objects as go
from IPython.display import HTML

# 1) Build the DataFrame
df_plot = features.copy()
df_plot['Cluster'] = eda.loc[features.index, 'Cluster']

# 2) Compute centroids in original units
centroids = kmeans.cluster_centers_
centroids_x = centroids[:, 0] * X.std(axis=0)[0] + X.mean(axis=0)[0]
centroids_y = centroids[:, 1] * X.std(axis=0)[1] + X.mean(axis=0)[1]

# 3) Create an interactive Plotly Figure
fig = px.scatter(
    df_plot,
    x='SALARY',
    y='MAX_YEARS_EXPERIENCE',
    color='Cluster',
    title="KMeans Clustering by Salary and Max Years Experience",
    labels={
        'SALARY': 'Salary',
        'MAX_YEARS_EXPERIENCE': 'Max Years Experience',
        'Cluster': 'Cluster'
    },
    width=800,
    height=500,
)

# add centroids
fig.add_trace(
    go.Scatter(
        x=centroids_x,
        y=centroids_y,
        mode='markers',
        marker=dict(symbol='x', size=18, color='black',
                    line=dict(width=2, color='white')),
        name='Centroids'
    )
)

fig.update_layout(
    autosize=True,
    height=800,  
    margin=dict(l=20, r=20, t=50, b=20)
)

fig.write_html(
    "figures/analytics_plot1.html",
    include_plotlyjs=True,   
    full_html=False
)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[3], line 6
      3 from IPython.display import HTML
      5 # 1) Build the DataFrame
----> 6 df_plot = features.copy()
      7 df_plot['Cluster'] = eda.loc[features.index, 'Cluster']
      9 # 2) Compute centroids in original units

NameError: name 'features' is not defined

Here we have 4 cluster groups. Group 0, which represent as green have lower salary, mostly under 150k, and max years experience in 2-5 years, it is likely Likely junior to mid-level employees with moderate pay. Group 1 with orange, has medium to high salary, wide range from $100k–$500k and with narrow range ~3 years, they are suggests specialized or high-paying roles with short experience — possibly fast-track promotions or high-demand fields. cluster 2 are low salary and experience from 0-4 years, they are clearly entry level employee. cluster 3 has medium salary, mostly under 200k with higher experiences, like 6-13 eyars. They probably are senior professionals with more experience but not the highest salaries.

Code
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import plotly.graph_objects as go

# Prepare features & target
features = eda[['MIN_YEARS_EXPERIENCE', 'MAX_YEARS_EXPERIENCE']].apply(pd.to_numeric, errors='coerce')
features = features.dropna()
X = features
y = eda.loc[X.index, 'SALARY']

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=688)

# Fit model & predict
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Metrics (optional, but handy)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"MSE: {mse:.2f}, R²: {r2:.3f}")

# Define min/max for the identity line
min_val = y_test.min()
max_val = y_test.max()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 8
      5 import plotly.graph_objects as go
      7 # Prepare features & target
----> 8 features = eda[['MIN_YEARS_EXPERIENCE', 'MAX_YEARS_EXPERIENCE']].apply(pd.to_numeric, errors='coerce')
      9 features = features.dropna()
     10 X = features

NameError: name 'eda' is not defined
Code
fig = go.Figure([
    go.Scatter(
        x=y_test,
        y=y_pred,
        mode='markers',
        marker=dict(color='skyblue', opacity=0.6),
        name='Predicted vs Actual'
    ),
    go.Scatter(
        x=[min_val, max_val],
        y=[min_val, max_val],
        mode='lines',
        line=dict(color='red', dash='dash'),
        name='Ideal Fit'
    )
])

fig.update_layout(
    autosize=True,
    height=600,  # or whatever default height you prefer
    margin=dict(l=20, r=20, t=50, b=20)
)

fig.write_html(
    'figures/analytics_plot2.html',
    full_html=False,
    include_plotlyjs="cdn",
    config={"responsive": True}
)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[5], line 3
      1 fig = go.Figure([
      2     go.Scatter(
----> 3         x=y_test,
      4         y=y_pred,
      5         mode='markers',
      6         marker=dict(color='skyblue', opacity=0.6),
      7         name='Predicted vs Actual'
      8     ),
      9     go.Scatter(
     10         x=[min_val, max_val],
     11         y=[min_val, max_val],
     12         mode='lines',
     13         line=dict(color='red', dash='dash'),
     14         name='Ideal Fit'
     15     )
     16 ])
     18 fig.update_layout(
     19     autosize=True,
     20     height=600,  # or whatever default height you prefer
     21     margin=dict(l=20, r=20, t=50, b=20)
     22 )
     24 fig.write_html(
     25     'figures/analytics_plot2.html',
     26     full_html=False,
     27     include_plotlyjs="cdn",
     28     config={"responsive": True}
     29 )

NameError: name 'y_test' is not defined

This plot shows the Actual vs. Predicted Salary using a multiple linear regression model. The blue dots represent individual predictions, and the red dashed line is the ideal line where predicted = actual. Since most points lie very close to the red line, it means your model predicts salary very accurately, with minimal error and strong linear fit — likely reflected in a high R² score near 1.0.